Recent studies in bacteria using next-generation RNA sequencing technology have found that gene regulation is more complex than previously thought in these organisms. For example, bacteria have:
Although large amounts of RNA sequencing data are being generated, there is a lack of computational approaches for effectively integrating data generated from high-throughput technologies to gain deeper understanding of how bacteria control gene expression. The ultimate goal of this proposal is to develop smart computational methods to decipher gene regulation in bacteria. Control of transcription initiation is the first step in gene regulation. A promoter is a genomic region that determines when and how transcription of a gene is initiated. Gene expression in bacteria is also regulated by stopping gene transcription in response to specific signals. A terminator is a sequence that can stop transcription. For the duration of this research grant, we will focus on developing machine learning-based approaches to
To facilitate the uptake of our methods by the bacterial research community we will distribute these methods as containerized computational pipelines so that the approaches will be easily used by natural sciences researchers. A containerized pipeline is one that can be executed without having to install all the extra software used by the pipeline. This research is important not only to facilitate microbiology research so that our understanding of gene regulation in bacteria improves, but outcomes of this research could also potentially be exploited by a variety of industries (e.g., food, pharmaceutical, and biotechnology). For example, identified sRNAs could be used as switches to control the bacterial production of certain substances in the food industry, or as targets for the development of novel antibiotics.